Method and device for predicting drug resistance phenotype of mycobacterium tuberculosis, and computer device

By predicting drug resistance phenotypes of Mycobacterium tuberculosis using gene sequencing data and protein structure change data, this technology solves the problems of long detection times or inability to identify unknown mutations in existing technologies, and achieves rapid and accurate drug resistance assessment.

CN119541635BActive Publication Date: 2026-06-26SANSURE BIOTECH INC +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SANSURE BIOTECH INC
Filing Date
2024-11-08
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing methods for detecting drug resistance in Mycobacterium tuberculosis are time-consuming or unable to identify drug resistance phenotypes with unknown mutations, resulting in poor detection results.

Method used

By identifying unexisting mutation sites in the mutation phenotype annotation database using gene sequencing data, and inputting protein structure change data into the drug resistance phenotype prediction model, combined with existing mutation site phenotype annotation information, the drug resistance phenotype of Mycobacterium tuberculosis is predicted.

Benefits of technology

It improves the efficiency of predicting drug resistance phenotypes in Mycobacterium tuberculosis, effectively determines whether unknown mutation sites lead to drug resistance, and enhances the speed and accuracy of detection.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to a mycobacterium tuberculosis drug resistance phenotype prediction method and device and a computer device. The method comprises the following steps: determining a to-be-predicted mutation site which does not exist in a mutation phenotype annotation database from at least one mutation site of a target gene fragment of to-be-processed mycobacterium tuberculosis according to gene sequencing data of the mycobacterium tuberculosis; converting protein structure change data of the to-be-predicted mutation site according to protein structure data corresponding to the to-be-predicted mutation site; inputting the protein structure change data of the to-be-predicted mutation site into a drug resistance phenotype prediction model to obtain a predicted drug resistance phenotype of the to-be-predicted mutation site; and determining a drug resistance phenotype prediction result of the to-be-processed mycobacterium tuberculosis based on the predicted drug resistance phenotype of the to-be-predicted mutation site and drug resistance phenotypes of the mutation sites except the to-be-predicted mutation site. The method can effectively improve the mycobacterium tuberculosis drug resistance phenotype prediction efficiency.
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